PyViz Application¶
dede1.
import os
import numpy as np
import xarray as xr
import cartopy
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
cartopy.config['data_dir'] = os.getenv('CARTOPY_DIR', cartopy.config.get('data_dir'))
import cmocean
import holoviews as hv
from holoviews import opts, dim
import geoviews as gv
from geoviews import tile_sources as gvts
import geoviews.feature as gf
from cartopy import crs as ccrs
import hvplot.xarray
import panel as pn
import warnings
warnings.filterwarnings("ignore", category=RuntimeWarning)
gv.extension('bokeh')
year = 2018
# GBR4
base_url = "http://thredds.ereefs.aims.gov.au/thredds/dodsC/s3://aims-ereefs-public-prod/derived/ncaggregate/ereefs/GBR4_H2p0_B3p1_Cq3b_Dhnd/daily-monthly/EREEFS_AIMS-CSIRO_GBR4_H2p0_B3p1_Cq3b_Dhnd_bgc_daily-monthly-"
biofiles = [f"{base_url}{year}-{month:02}.nc" for month in range(1, 2)]
biofiles
['http://thredds.ereefs.aims.gov.au/thredds/dodsC/s3://aims-ereefs-public-prod/derived/ncaggregate/ereefs/GBR4_H2p0_B3p1_Cq3b_Dhnd/daily-monthly/EREEFS_AIMS-CSIRO_GBR4_H2p0_B3p1_Cq3b_Dhnd_bgc_daily-monthly-2018-01.nc']
ds_bio = xr.open_mfdataset(biofiles)
ds_bio
<xarray.Dataset>
Dimensions: (k: 17, latitude: 723, longitude: 491, time: 31)
Coordinates:
zc (k) float64 dask.array<chunksize=(17,), meta=np.ndarray>
* time (time) datetime64[ns] 2018-01-01T02:00:00 ... 2018-01-31...
* latitude (latitude) float64 -28.7 -28.67 -28.64 ... -7.066 -7.036
* longitude (longitude) float64 142.2 142.2 142.2 ... 156.8 156.8 156.9
Dimensions without coordinates: k
Data variables: (12/101)
alk (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
BOD (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
Chl_a_sum (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
CO32 (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
DIC (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
DIN (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
... ...
SGH_N (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
SGH_N_pr (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
SGHROOT_N (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
SGROOT_N (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
TSSM (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
Zenith2D (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
Attributes: (12/20)
Conventions: CF-1.0
NCO: netCDF Operators version 4.7.7 (Homepage...
RunID: 2
_CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention
aims_ncaggregate_buildDate: 2020-08-21T23:07:30+10:00
aims_ncaggregate_datasetId: products__ncaggregate__ereefs__GBR4_H2p0...
... ...
paramfile: /home/bai155/EMS_solar2/gbr4_H2p0_B3p1_C...
paramhead: eReefs 4 km grid. SOURCE Catchments with...
technical_guide_link: https://eatlas.org.au/pydio/public/aims-...
technical_guide_publish_date: 2020-08-18
title: eReefs AIMS-CSIRO GBR4 BioGeoChemical 3....
DODS_EXTRA.Unlimited_Dimension: timexarray.Dataset
- k: 17
- latitude: 723
- longitude: 491
- time: 31
- zc(k)float64dask.array<chunksize=(17,), meta=np.ndarray>
- long_name :
- Z coordinate
- _CoordinateAxisType :
- Height
- _CoordinateZisPositive :
- up
- units :
- m
- positive :
- up
- axis :
- Z
- coordinate_type :
- Z
Array Chunk Bytes 136 B 136 B Shape (17,) (17,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - time(time)datetime64[ns]2018-01-01T02:00:00 ... 2018-01-...
- long_name :
- Time
- standard_name :
- time
- coordinate_type :
- time
- _CoordinateAxisType :
- Time
- _ChunkSizes :
- 1024
array(['2018-01-01T02:00:00.000000000', '2018-01-02T02:00:00.000000000', '2018-01-03T02:00:00.000000000', '2018-01-04T02:00:00.000000000', '2018-01-05T02:00:00.000000000', '2018-01-06T02:00:00.000000000', '2018-01-07T02:00:00.000000000', '2018-01-08T02:00:00.000000000', '2018-01-09T02:00:00.000000000', '2018-01-10T02:00:00.000000000', '2018-01-11T02:00:00.000000000', '2018-01-12T02:00:00.000000000', '2018-01-13T02:00:00.000000000', '2018-01-14T02:00:00.000000000', '2018-01-15T02:00:00.000000000', '2018-01-16T02:00:00.000000000', '2018-01-17T02:00:00.000000000', '2018-01-18T02:00:00.000000000', '2018-01-19T02:00:00.000000000', '2018-01-20T02:00:00.000000000', '2018-01-21T02:00:00.000000000', '2018-01-22T02:00:00.000000000', '2018-01-23T02:00:00.000000000', '2018-01-24T02:00:00.000000000', '2018-01-25T02:00:00.000000000', '2018-01-26T02:00:00.000000000', '2018-01-27T02:00:00.000000000', '2018-01-28T02:00:00.000000000', '2018-01-29T02:00:00.000000000', '2018-01-30T02:00:00.000000000', '2018-01-31T02:00:00.000000000'], dtype='datetime64[ns]') - latitude(latitude)float64-28.7 -28.67 ... -7.066 -7.036
- units :
- degrees_north
- long_name :
- Latitude
- standard_name :
- latitude
- projection :
- geographic
- coordinate_type :
- latitude
- _CoordinateAxisType :
- Lat
array([-28.696022, -28.666022, -28.636022, ..., -7.096022, -7.066022, -7.036022]) - longitude(longitude)float64142.2 142.2 142.2 ... 156.8 156.9
- units :
- degrees_east
- long_name :
- Longitude
- standard_name :
- longitude
- projection :
- geographic
- coordinate_type :
- longitude
- _CoordinateAxisType :
- Lon
array([142.168788, 142.198788, 142.228788, ..., 156.808788, 156.838788, 156.868788])
- alk(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- alk
- units :
- mmol m-3
- long_name :
- Total alkalinity
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - BOD(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- BOD
- units :
- mg O m-3
- long_name :
- Biochemical Oxygen Demand
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Chl_a_sum(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Chl_a_sum
- units :
- mg Chl m-3
- long_name :
- Total Chlorophyll
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - CO32(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- CO32
- units :
- mmol m-3
- long_name :
- Carbonate
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DIC(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DIC
- units :
- mg C m-3
- long_name :
- Dissolved Inorganic Carbon
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DIN(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DIN
- units :
- mg N m-3
- long_name :
- Dissolved Inorganic Nitrogen
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DIP(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DIP
- units :
- mg P m-3
- long_name :
- Dissolved Inorganic Phosphorus
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DOR_C(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DOR_C
- units :
- mg C m-3
- long_name :
- Dissolved Organic Carbon
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DOR_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DOR_N
- units :
- mg N m-3
- long_name :
- Dissolved Organic Nitrogen
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DOR_P(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DOR_P
- units :
- mg P m-3
- long_name :
- Dissolved Organic Phosphorus
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Dust(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Dust
- units :
- kg m-3
- long_name :
- Dust
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - EFI(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- EFI
- units :
- kg m-3
- long_name :
- Ecology Fine Inorganics
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - FineSed(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- FineSed
- units :
- kg m-3
- long_name :
- FineSed
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Fluorescence(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Fluorescence
- units :
- mg chla m-3
- long_name :
- Simulated Fluorescence
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - HCO3(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- HCO3
- units :
- mmol m-3
- long_name :
- Bicarbonate
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Kd_490(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Kd_490
- units :
- m-1
- long_name :
- Vert. att. at 490 nm
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - MPB_Chl(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- MPB_Chl
- units :
- mg Chl m-3
- long_name :
- Microphytobenthos chlorophyll
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - MPB_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- MPB_N
- units :
- mg N m-3
- long_name :
- Microphytobenthos N
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Mud-carbonate(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Mud-carbonate
- units :
- kg m-3
- long_name :
- Mud-carbonate
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Mud-mineral(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Mud-mineral
- units :
- kg m-3
- long_name :
- Mud-mineral
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Nfix(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Nfix
- units :
- mg N m-3 s-1
- long_name :
- N2 fixation
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - NH4(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- NH4
- units :
- mg N m-3
- long_name :
- Ammonia
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - NO3(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- NO3
- units :
- mg N m-3
- long_name :
- Nitrate
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - omega_ar(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- omega_ar
- units :
- nil
- long_name :
- Aragonite saturation state
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Oxy_sat(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Oxy_sat
- units :
- %
- long_name :
- Oxygen saturation percent
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Oxygen(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Oxygen
- units :
- mg O m-3
- long_name :
- Dissolved Oxygen
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - P_Prod(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- P_Prod
- units :
- mg C m-3 d-1
- long_name :
- Phytoplankton total productivity
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PAR(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PAR
- units :
- mol photon m-2 s-1
- long_name :
- Av. PAR in layer
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PAR_z(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PAR_z
- units :
- mol photon m-2 s-1
- long_name :
- Downwelling PAR at top of layer
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - pco2surf(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- pco2surf
- units :
- ppmv
- long_name :
- oceanic pCO2 (ppmv)
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PH(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PH
- units :
- log(mM)
- long_name :
- PH
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PhyL_Chl(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PhyL_Chl
- units :
- mg Chl m-3
- long_name :
- Large Phytoplankton chlorophyll
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PhyL_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PhyL_N
- units :
- mg N m-3
- long_name :
- Large Phytoplankton N
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PhyS_Chl(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PhyS_Chl
- units :
- mg Chl m-3
- long_name :
- Small Phytoplankton chlorophyll
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PhyS_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PhyS_N
- units :
- mg N m-3
- long_name :
- Small Phytoplankton N
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PhyS_NR(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PhyS_NR
- units :
- mg N m-3
- long_name :
- Small Phytoplankton N reserve
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PIP(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PIP
- units :
- mg P m-3
- long_name :
- Particulate Inorganic Phosphorus
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - salt(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- salt
- units :
- PSU
- long_name :
- Salinity
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - TC(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- TC
- units :
- mg C m-3
- long_name :
- Total C
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - temp(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- temp
- units :
- degrees C
- long_name :
- Temperature
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - TN(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- TN
- units :
- mg N m-3
- long_name :
- Total N
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - TP(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- TP
- units :
- mg P m-3
- long_name :
- Total P
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Tricho_Chl(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Tricho_Chl
- units :
- mg Chl m-3
- long_name :
- Trichodesmium chlorophyll
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Tricho_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Tricho_N
- units :
- mg N m-3
- long_name :
- Trichodesmium Nitrogen
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Z_grazing(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Z_grazing
- units :
- mg C m-3 d-1
- long_name :
- Zooplankton total grazing
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - ZooL_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- ZooL_N
- units :
- mg N m-3
- long_name :
- Large Zooplankton N
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - ZooS_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- ZooS_N
- units :
- mg N m-3
- long_name :
- Small Zooplankton N
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - CH_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- CH_N
- units :
- g N m-2
- long_name :
- Coral host N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - CS_bleach(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- CS_bleach
- units :
- d-1
- long_name :
- Coral bleach rate
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - CS_Chl(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- CS_Chl
- units :
- mg Chl m-2
- long_name :
- Coral symbiont Chl
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - CS_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- CS_N
- units :
- mg N m-2
- long_name :
- Coral symbiont N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - EpiPAR_sg(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- EpiPAR_sg
- units :
- mol photon m-2 d-1
- long_name :
- Light intensity above seagrass
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - eta(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- eta
- units :
- metre
- long_name :
- Surface Elevation
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - MA_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- MA_N
- units :
- g N m-2
- long_name :
- Macroalgae N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - MA_N_pr(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- MA_N_pr
- units :
- mg N m-2 d-1
- long_name :
- Macroalgae net production
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - month_EpiPAR_sg(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- month_EpiPAR_sg
- units :
- mol photon m-2
- long_name :
- Monthly dose light above seagrass
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_400(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_400
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 400 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_410(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_410
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 410 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_412(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_412
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 412 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_443(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_443
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 443 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_470(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_470
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 470 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_486(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_486
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 486 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_488(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_488
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 488 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_490(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_490
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 490 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_510(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_510
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 510 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_531(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_531
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 531 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_547(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_547
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 547 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_551(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_551
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 551 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_555(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_555
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 555 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_560(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_560
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 560 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_590(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_590
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 590 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_620(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_620
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 620 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_640(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_640
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 640 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_645(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_645
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 645 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_665(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_665
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 665 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_667(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_667
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 667 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_671(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_671
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 671 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_673(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_673
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 673 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_678(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_678
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 678 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_681(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_681
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 681 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_709(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_709
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 709 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_745(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_745
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 745 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_748(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_748
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 748 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_754(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_754
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 754 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_761(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_761
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 761 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_764(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_764
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 764 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_767(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_767
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 767 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_778(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_778
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 778 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Secchi(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- Secchi
- units :
- m
- long_name :
- Secchi from 488 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SG_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SG_N
- units :
- g N m-2
- long_name :
- Seagrass N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SG_N_pr(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SG_N_pr
- units :
- mg N m-2 d-1
- long_name :
- Seagrass net production
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SG_shear_mort(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SG_shear_mort
- units :
- g N m-2 d-1
- long_name :
- Seagrass shear stress mort
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGD_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGD_N
- units :
- g N m-2
- long_name :
- Deep seagrass N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGD_N_pr(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGD_N_pr
- units :
- mg N m-2 d-1
- long_name :
- Deep seagrass net production
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGD_shear_mort(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGD_shear_mort
- units :
- g N m-2 d-1
- long_name :
- Deep seagrass shear stress mort
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGH_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGH_N
- units :
- g N m-2
- long_name :
- Halophila N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGH_N_pr(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGH_N_pr
- units :
- mg N m-2 d-1
- long_name :
- Halophila net production
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGHROOT_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGHROOT_N
- units :
- g N m-2
- long_name :
- Halophila root N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGROOT_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGROOT_N
- units :
- g N m-2
- long_name :
- Seagrass root N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - TSSM(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- TSSM
- units :
- g TSS m-3
- long_name :
- TSS from 645 nm (Petus et al., 2014)
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Zenith2D(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- Zenith2D
- units :
- rad
- long_name :
- Solar zenith
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray
- Conventions :
- CF-1.0
- NCO :
- netCDF Operators version 4.7.7 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)
- RunID :
- 2
- _CoordSysBuilder :
- ucar.nc2.dataset.conv.CF1Convention
- aims_ncaggregate_buildDate :
- 2020-08-21T23:07:30+10:00
- aims_ncaggregate_datasetId :
- products__ncaggregate__ereefs__GBR4_H2p0_B3p1_Cq3b_Dhnd__daily-monthly/EREEFS_AIMS-CSIRO_GBR4_H2p0_B3p1_Cq3b_Dhnd_bgc_daily-monthly-2018-01
- aims_ncaggregate_firstDate :
- 2018-01-01T12:00:00+10:00
- aims_ncaggregate_inputs :
- [products__ncaggregate__ereefs__GBR4_H2p0_B3p1_Cq3b_Dhnd__raw/EREEFS_AIMS-CSIRO_GBR4_H2p0_B3p1_Cq3b_Dhnd_bgc_raw_2018-01::MD5:922cfd031369e604eab88561e411dc0e]
- aims_ncaggregate_lastDate :
- 2018-01-31T12:00:00+10:00
- codehead :
- CSIRO Environmental Modelling Suite
- description :
- Regridding of daily input data (from eReefs AIMS-CSIRO GBR4 BioGeoChemical 3.1 subset) from curvilinear (per input data) to rectilinear via inverse weighted distance from up to 4 closest cells.
- ems_version :
- v1.1.1 rev(6244M)
- history :
- Tue Oct 8 15:38:27 2019: ncatted -a positive,botz,o,char,up -a missing_value,botz,o,double,99. -a outside,botz,o,double,-9999. gbr4_bgc_all_simple_2018-01.nc 2020-08-20T23:45:30+10:00: vendor: AIMS; processing: None summaries 2020-08-21T23:07:30+10:00: vendor: AIMS; processing: None summaries
- metadata_link :
- https://eatlas.org.au/data/uuid/61f3a6df-2c4a-46b6-ab62-3f3a9bf4e87a
- paramfile :
- /home/bai155/EMS_solar2/gbr4_H2p0_B3p1_Cb/tran/GBR4_H2p0_B3p1_Cq3b_Dhnd.tran
- paramhead :
- eReefs 4 km grid. SOURCE Catchments with 2019 condition from Dec 1, 2010 to June,30, 2018, Empirical SOURCE with 2019 condition, Jul 1, 2018 to April 30, 2019. More details of naming protocol at: eReefs.info.
- technical_guide_link :
- https://eatlas.org.au/pydio/public/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf
- technical_guide_publish_date :
- 2020-08-18
- title :
- eReefs AIMS-CSIRO GBR4 BioGeoChemical 3.1 (baseline catchment conditions) daily aggregation
- DODS_EXTRA.Unlimited_Dimension :
- time
# base_url2 = "http://thredds.ereefs.aims.gov.au/thredds/dodsC/s3://aims-ereefs-public-prod/derived/ncaggregate/ereefs/gbr4_v2/daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-"
# hydrofiles = [f"{base_url2}{year}-{month:02}.nc" for month in range(1, 2)]
# hydrofiles
# ds_hydro = xr.open_mfdataset(hydrofiles)
# ds_hydro
base_map = gvts.EsriImagery
crs = ccrs.PlateCarree()
ds_bio.coords['k'] = ('zc',ds_bio.zc)
ds_bio = ds_bio.swap_dims({'zc':'k'})
ds_bio = ds_bio.drop(['zc'])
ds_bio
<xarray.Dataset>
Dimensions: (k: 17, latitude: 723, longitude: 491, time: 31)
Coordinates:
* time (time) datetime64[ns] 2018-01-01T02:00:00 ... 2018-01-31...
* latitude (latitude) float64 -28.7 -28.67 -28.64 ... -7.066 -7.036
* longitude (longitude) float64 142.2 142.2 142.2 ... 156.8 156.8 156.9
* k (k) float64 -145.0 -120.0 -103.0 -88.0 ... -3.0 -1.5 -0.5
Data variables: (12/101)
alk (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
BOD (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
Chl_a_sum (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
CO32 (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
DIC (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
DIN (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
... ...
SGH_N (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
SGH_N_pr (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
SGHROOT_N (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
SGROOT_N (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
TSSM (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
Zenith2D (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
Attributes: (12/20)
Conventions: CF-1.0
NCO: netCDF Operators version 4.7.7 (Homepage...
RunID: 2
_CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention
aims_ncaggregate_buildDate: 2020-08-21T23:07:30+10:00
aims_ncaggregate_datasetId: products__ncaggregate__ereefs__GBR4_H2p0...
... ...
paramfile: /home/bai155/EMS_solar2/gbr4_H2p0_B3p1_C...
paramhead: eReefs 4 km grid. SOURCE Catchments with...
technical_guide_link: https://eatlas.org.au/pydio/public/aims-...
technical_guide_publish_date: 2020-08-18
title: eReefs AIMS-CSIRO GBR4 BioGeoChemical 3....
DODS_EXTRA.Unlimited_Dimension: timexarray.Dataset
- k: 17
- latitude: 723
- longitude: 491
- time: 31
- time(time)datetime64[ns]2018-01-01T02:00:00 ... 2018-01-...
- long_name :
- Time
- standard_name :
- time
- coordinate_type :
- time
- _CoordinateAxisType :
- Time
- _ChunkSizes :
- 1024
array(['2018-01-01T02:00:00.000000000', '2018-01-02T02:00:00.000000000', '2018-01-03T02:00:00.000000000', '2018-01-04T02:00:00.000000000', '2018-01-05T02:00:00.000000000', '2018-01-06T02:00:00.000000000', '2018-01-07T02:00:00.000000000', '2018-01-08T02:00:00.000000000', '2018-01-09T02:00:00.000000000', '2018-01-10T02:00:00.000000000', '2018-01-11T02:00:00.000000000', '2018-01-12T02:00:00.000000000', '2018-01-13T02:00:00.000000000', '2018-01-14T02:00:00.000000000', '2018-01-15T02:00:00.000000000', '2018-01-16T02:00:00.000000000', '2018-01-17T02:00:00.000000000', '2018-01-18T02:00:00.000000000', '2018-01-19T02:00:00.000000000', '2018-01-20T02:00:00.000000000', '2018-01-21T02:00:00.000000000', '2018-01-22T02:00:00.000000000', '2018-01-23T02:00:00.000000000', '2018-01-24T02:00:00.000000000', '2018-01-25T02:00:00.000000000', '2018-01-26T02:00:00.000000000', '2018-01-27T02:00:00.000000000', '2018-01-28T02:00:00.000000000', '2018-01-29T02:00:00.000000000', '2018-01-30T02:00:00.000000000', '2018-01-31T02:00:00.000000000'], dtype='datetime64[ns]') - latitude(latitude)float64-28.7 -28.67 ... -7.066 -7.036
- units :
- degrees_north
- long_name :
- Latitude
- standard_name :
- latitude
- projection :
- geographic
- coordinate_type :
- latitude
- _CoordinateAxisType :
- Lat
array([-28.696022, -28.666022, -28.636022, ..., -7.096022, -7.066022, -7.036022]) - longitude(longitude)float64142.2 142.2 142.2 ... 156.8 156.9
- units :
- degrees_east
- long_name :
- Longitude
- standard_name :
- longitude
- projection :
- geographic
- coordinate_type :
- longitude
- _CoordinateAxisType :
- Lon
array([142.168788, 142.198788, 142.228788, ..., 156.808788, 156.838788, 156.868788]) - k(k)float64-145.0 -120.0 -103.0 ... -1.5 -0.5
array([-145. , -120. , -103. , -88. , -73. , -60. , -49. , -39.5 , -31. , -23.75, -17.75, -12.75, -8.8 , -5.55, -3. , -1.5 , -0.5 ])
- alk(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- alk
- units :
- mmol m-3
- long_name :
- Total alkalinity
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - BOD(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- BOD
- units :
- mg O m-3
- long_name :
- Biochemical Oxygen Demand
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Chl_a_sum(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Chl_a_sum
- units :
- mg Chl m-3
- long_name :
- Total Chlorophyll
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - CO32(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- CO32
- units :
- mmol m-3
- long_name :
- Carbonate
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DIC(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DIC
- units :
- mg C m-3
- long_name :
- Dissolved Inorganic Carbon
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DIN(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DIN
- units :
- mg N m-3
- long_name :
- Dissolved Inorganic Nitrogen
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DIP(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DIP
- units :
- mg P m-3
- long_name :
- Dissolved Inorganic Phosphorus
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DOR_C(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DOR_C
- units :
- mg C m-3
- long_name :
- Dissolved Organic Carbon
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DOR_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DOR_N
- units :
- mg N m-3
- long_name :
- Dissolved Organic Nitrogen
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - DOR_P(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- DOR_P
- units :
- mg P m-3
- long_name :
- Dissolved Organic Phosphorus
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Dust(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Dust
- units :
- kg m-3
- long_name :
- Dust
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - EFI(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- EFI
- units :
- kg m-3
- long_name :
- Ecology Fine Inorganics
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - FineSed(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- FineSed
- units :
- kg m-3
- long_name :
- FineSed
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Fluorescence(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Fluorescence
- units :
- mg chla m-3
- long_name :
- Simulated Fluorescence
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - HCO3(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- HCO3
- units :
- mmol m-3
- long_name :
- Bicarbonate
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Kd_490(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Kd_490
- units :
- m-1
- long_name :
- Vert. att. at 490 nm
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - MPB_Chl(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- MPB_Chl
- units :
- mg Chl m-3
- long_name :
- Microphytobenthos chlorophyll
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - MPB_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- MPB_N
- units :
- mg N m-3
- long_name :
- Microphytobenthos N
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Mud-carbonate(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Mud-carbonate
- units :
- kg m-3
- long_name :
- Mud-carbonate
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Mud-mineral(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Mud-mineral
- units :
- kg m-3
- long_name :
- Mud-mineral
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Nfix(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Nfix
- units :
- mg N m-3 s-1
- long_name :
- N2 fixation
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - NH4(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- NH4
- units :
- mg N m-3
- long_name :
- Ammonia
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - NO3(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- NO3
- units :
- mg N m-3
- long_name :
- Nitrate
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - omega_ar(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- omega_ar
- units :
- nil
- long_name :
- Aragonite saturation state
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Oxy_sat(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Oxy_sat
- units :
- %
- long_name :
- Oxygen saturation percent
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Oxygen(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Oxygen
- units :
- mg O m-3
- long_name :
- Dissolved Oxygen
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - P_Prod(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- P_Prod
- units :
- mg C m-3 d-1
- long_name :
- Phytoplankton total productivity
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PAR(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PAR
- units :
- mol photon m-2 s-1
- long_name :
- Av. PAR in layer
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PAR_z(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PAR_z
- units :
- mol photon m-2 s-1
- long_name :
- Downwelling PAR at top of layer
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - pco2surf(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- pco2surf
- units :
- ppmv
- long_name :
- oceanic pCO2 (ppmv)
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PH(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PH
- units :
- log(mM)
- long_name :
- PH
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PhyL_Chl(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PhyL_Chl
- units :
- mg Chl m-3
- long_name :
- Large Phytoplankton chlorophyll
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PhyL_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PhyL_N
- units :
- mg N m-3
- long_name :
- Large Phytoplankton N
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PhyS_Chl(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PhyS_Chl
- units :
- mg Chl m-3
- long_name :
- Small Phytoplankton chlorophyll
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PhyS_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PhyS_N
- units :
- mg N m-3
- long_name :
- Small Phytoplankton N
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PhyS_NR(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PhyS_NR
- units :
- mg N m-3
- long_name :
- Small Phytoplankton N reserve
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - PIP(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- PIP
- units :
- mg P m-3
- long_name :
- Particulate Inorganic Phosphorus
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - salt(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- salt
- units :
- PSU
- long_name :
- Salinity
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - TC(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- TC
- units :
- mg C m-3
- long_name :
- Total C
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - temp(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- temp
- units :
- degrees C
- long_name :
- Temperature
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - TN(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- TN
- units :
- mg N m-3
- long_name :
- Total N
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - TP(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- TP
- units :
- mg P m-3
- long_name :
- Total P
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Tricho_Chl(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Tricho_Chl
- units :
- mg Chl m-3
- long_name :
- Trichodesmium chlorophyll
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Tricho_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Tricho_N
- units :
- mg N m-3
- long_name :
- Trichodesmium Nitrogen
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Z_grazing(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- Z_grazing
- units :
- mg C m-3 d-1
- long_name :
- Zooplankton total grazing
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - ZooL_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- ZooL_N
- units :
- mg N m-3
- long_name :
- Large Zooplankton N
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - ZooS_N(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- short_name :
- ZooS_N
- units :
- mg N m-3
- long_name :
- Small Zooplankton N
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 748.33 MB 748.33 MB Shape (31, 17, 723, 491) (31, 17, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - CH_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- CH_N
- units :
- g N m-2
- long_name :
- Coral host N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - CS_bleach(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- CS_bleach
- units :
- d-1
- long_name :
- Coral bleach rate
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - CS_Chl(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- CS_Chl
- units :
- mg Chl m-2
- long_name :
- Coral symbiont Chl
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - CS_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- CS_N
- units :
- mg N m-2
- long_name :
- Coral symbiont N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - EpiPAR_sg(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- EpiPAR_sg
- units :
- mol photon m-2 d-1
- long_name :
- Light intensity above seagrass
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - eta(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- eta
- units :
- metre
- long_name :
- Surface Elevation
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - MA_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- MA_N
- units :
- g N m-2
- long_name :
- Macroalgae N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - MA_N_pr(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- MA_N_pr
- units :
- mg N m-2 d-1
- long_name :
- Macroalgae net production
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - month_EpiPAR_sg(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- month_EpiPAR_sg
- units :
- mol photon m-2
- long_name :
- Monthly dose light above seagrass
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_400(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_400
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 400 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_410(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_410
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 410 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_412(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_412
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 412 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_443(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_443
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 443 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_470(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_470
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 470 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_486(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_486
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 486 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_488(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_488
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 488 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_490(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_490
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 490 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_510(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_510
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 510 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_531(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_531
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 531 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_547(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_547
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 547 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_551(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_551
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 551 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_555(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_555
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 555 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_560(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_560
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 560 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_590(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_590
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 590 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_620(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_620
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 620 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_640(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_640
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 640 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_645(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_645
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 645 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_665(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_665
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 665 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_667(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_667
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 667 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_671(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_671
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 671 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_673(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_673
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 673 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_678(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_678
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 678 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_681(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_681
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 681 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_709(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_709
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 709 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_745(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_745
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 745 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_748(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_748
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 748 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_754(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_754
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 754 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_761(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_761
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 761 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_764(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_764
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 764 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_767(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_767
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 767 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - R_778(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- R_778
- units :
- sr-1
- long_name :
- Remote-sensing reflectance @ 778 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Secchi(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- Secchi
- units :
- m
- long_name :
- Secchi from 488 nm
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SG_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SG_N
- units :
- g N m-2
- long_name :
- Seagrass N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SG_N_pr(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SG_N_pr
- units :
- mg N m-2 d-1
- long_name :
- Seagrass net production
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SG_shear_mort(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SG_shear_mort
- units :
- g N m-2 d-1
- long_name :
- Seagrass shear stress mort
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGD_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGD_N
- units :
- g N m-2
- long_name :
- Deep seagrass N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGD_N_pr(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGD_N_pr
- units :
- mg N m-2 d-1
- long_name :
- Deep seagrass net production
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGD_shear_mort(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGD_shear_mort
- units :
- g N m-2 d-1
- long_name :
- Deep seagrass shear stress mort
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGH_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGH_N
- units :
- g N m-2
- long_name :
- Halophila N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGH_N_pr(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGH_N_pr
- units :
- mg N m-2 d-1
- long_name :
- Halophila net production
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGHROOT_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGHROOT_N
- units :
- g N m-2
- long_name :
- Halophila root N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - SGROOT_N(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- SGROOT_N
- units :
- g N m-2
- long_name :
- Seagrass root N
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - TSSM(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- TSSM
- units :
- g TSS m-3
- long_name :
- TSS from 645 nm (Petus et al., 2014)
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - Zenith2D(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- Zenith2D
- units :
- rad
- long_name :
- Solar zenith
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 44.02 MB 44.02 MB Shape (31, 723, 491) (31, 723, 491) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray
- Conventions :
- CF-1.0
- NCO :
- netCDF Operators version 4.7.7 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)
- RunID :
- 2
- _CoordSysBuilder :
- ucar.nc2.dataset.conv.CF1Convention
- aims_ncaggregate_buildDate :
- 2020-08-21T23:07:30+10:00
- aims_ncaggregate_datasetId :
- products__ncaggregate__ereefs__GBR4_H2p0_B3p1_Cq3b_Dhnd__daily-monthly/EREEFS_AIMS-CSIRO_GBR4_H2p0_B3p1_Cq3b_Dhnd_bgc_daily-monthly-2018-01
- aims_ncaggregate_firstDate :
- 2018-01-01T12:00:00+10:00
- aims_ncaggregate_inputs :
- [products__ncaggregate__ereefs__GBR4_H2p0_B3p1_Cq3b_Dhnd__raw/EREEFS_AIMS-CSIRO_GBR4_H2p0_B3p1_Cq3b_Dhnd_bgc_raw_2018-01::MD5:922cfd031369e604eab88561e411dc0e]
- aims_ncaggregate_lastDate :
- 2018-01-31T12:00:00+10:00
- codehead :
- CSIRO Environmental Modelling Suite
- description :
- Regridding of daily input data (from eReefs AIMS-CSIRO GBR4 BioGeoChemical 3.1 subset) from curvilinear (per input data) to rectilinear via inverse weighted distance from up to 4 closest cells.
- ems_version :
- v1.1.1 rev(6244M)
- history :
- Tue Oct 8 15:38:27 2019: ncatted -a positive,botz,o,char,up -a missing_value,botz,o,double,99. -a outside,botz,o,double,-9999. gbr4_bgc_all_simple_2018-01.nc 2020-08-20T23:45:30+10:00: vendor: AIMS; processing: None summaries 2020-08-21T23:07:30+10:00: vendor: AIMS; processing: None summaries
- metadata_link :
- https://eatlas.org.au/data/uuid/61f3a6df-2c4a-46b6-ab62-3f3a9bf4e87a
- paramfile :
- /home/bai155/EMS_solar2/gbr4_H2p0_B3p1_Cb/tran/GBR4_H2p0_B3p1_Cq3b_Dhnd.tran
- paramhead :
- eReefs 4 km grid. SOURCE Catchments with 2019 condition from Dec 1, 2010 to June,30, 2018, Empirical SOURCE with 2019 condition, Jul 1, 2018 to April 30, 2019. More details of naming protocol at: eReefs.info.
- technical_guide_link :
- https://eatlas.org.au/pydio/public/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf
- technical_guide_publish_date :
- 2020-08-18
- title :
- eReefs AIMS-CSIRO GBR4 BioGeoChemical 3.1 (baseline catchment conditions) daily aggregation
- DODS_EXTRA.Unlimited_Dimension :
- time
var = 'temp'
label = f'{ds_bio[var].long_name}: {ds_bio[var].units}'
mesh = ds_bio[var][:,:].hvplot.quadmesh(x='longitude',y='latitude',crs=crs, cmap='jet',
rasterize=True, groupby=list(ds_bio[var].dims[:2]), title=label, width=600,height=600)
overlay = (base_map * mesh).opts(active_tools=['wheel_zoom', 'pan'])
widgets = {dim: pn.widgets.Select for dim in ds_bio[var].dims[:2]}
dashboard = pn.pane.HoloViews(overlay, widgets=widgets).layout
dashboard
rho_vars = []
for var in ds_bio.data_vars:
if len(ds_bio[var].dims) > 0:
rho_vars.append(var)
var_select = pn.widgets.Select(name='Select variables:', options=rho_vars,
value='temp')
crs = ccrs.PlateCarree()
base_map_select = pn.widgets.Select(name='Choose underlying map:', options=gvts.tile_sources, value=gvts.EsriImagery)
color_select = pn.widgets.Select(name='Pick a colormap', options= sorted([
'cet_bgy', 'cet_bkr', 'cet_bgyw', 'cet_bky', 'cet_kbc', 'cet_coolwarm',
'cet_blues', 'cet_gwv', 'cet_bmw', 'cet_bjy', 'cet_bmy', 'cet_bwy', 'cet_kgy',
'cet_cwr', 'cet_gray', 'cet_dimgray', 'cet_fire', 'kb', 'cet_kg', 'cet_kr',
'cet_colorwheel', 'cet_isolium', 'cet_rainbow', 'cet_bgy_r', 'cet_bkr_r',
'cet_bgyw_r', 'cet_bky_r', 'cet_kbc_r', 'cet_coolwarm_r', 'cet_blues_r',
'cet_gwv_r', 'cet_bmw_r', 'cet_bjy_r', 'cet_bmy_r', 'cet_bwy_r', 'cet_kgy_r',
'cet_cwr_r', 'cet_gray_r', 'cet_dimgray_r', 'cet_fire_r', 'kb_r', 'cet_kg_r',
'cet_kr_r', 'cet_colorwheel_r', 'cet_isolium_r', 'cet_rainbow_r', 'jet'],
key=str.casefold), value='jet')
def plot(var=None, base_map=None, cmap='jet'):
base_map = base_map or base_map_select.value
var = var or var_select.value
label = f'{ds_bio[var].long_name}: {ds_bio[var].units}'
mesh = ds_bio[var].hvplot.quadmesh(x='longitude', y='latitude', rasterize=True, title=label,
width=600, height=600, crs=crs,
groupby=list(ds_bio[var].dims[:-2]),
cmap=cmap)
mesh = mesh.redim.default(**{d: ds_bio[d].values.max() for d in ds_bio[var].dims[:-2]})
overlay = (base_map * mesh.opts(alpha=0.9)).opts(active_tools=['wheel_zoom', 'pan'])
widgets = {dim: pn.widgets.Select for dim in ds_bio[var].dims[:-2]}
return pn.pane.HoloViews(overlay).layout #, widgets=widgets).layout
def on_var_select(event):
var = event.obj.value
dashboard[-1] = plot(var=var)
def on_base_map_select(event):
base_map = event.obj.value
dashboard[-1] = plot(base_map=base_map)
def on_color_select(event):
cmap = event.obj.value
dashboard[-1] = plot(cmap=cmap)
var_select.param.watch(on_var_select, parameter_names=['value']);
base_map_select.param.watch(on_base_map_select, parameter_names=['value']);
color_select.param.watch(on_color_select, parameter_names=['value']);
# dashboard = pn.Column(var_select, base_map_select, plot(var_select.value))
widget = pn.widgets.StaticText(name='', value='High-level dashboarding solution for interactive visualisation',
style={'font-size': "14px", 'font-style': "bold"})
selection_widget = pn.Row(var_select, color_select, base_map_select)
dashboard = pn.Column(widget, selection_widget, plot(var_select.value))
box = pn.WidgetBox('# eReefs App', dashboard)
box.servable()
As you can see, the resulting object is rendered in the notebook (above), and it’s usable as long as you have Python running on this code. You can also launch this app as a standalone server outside of the notebook, because we’ve marked the relevant object .servable(). That declaration means that if someone later runs this notebook as a server process (using panel serve --show ereefs_app.ipynb), your browser will open a separate window with the serveable object ready to explore or share, just like the screenshot at the top of this notebook.
#! panel serve --show --port 5009 ereefs_app.ipynb
This web page was generated from a Jupyter notebook and not all interactivity will work on this website.
- 1
Signell & Pothina: Analysis and Visualization of Coastal Ocean Model Data in the Cloud, 2019.
